Optimal Energy Management Modelling Of A Grid-Connection Micro-Hydrokinetic With Pumped Hydro Storage

Abstract

The pressure on greenhouse gases (GHGs) emission reduction and energy deficit crisis are of global concern. The ever increasing energy demand due to population growth as well as industrial and commercial business developments, leads to energy deficit crisis for electric utility operators around the globe. This generates an increased probability of grid instability and blackout challenges. Hence, this promotes the requirement for the additional fossil fuel power plants, leading to electricity price increase for consumers, due to high investment cost and the rising fossil fuels prices. The exploitation of an onsite grid-connected renewable energy (RE) system may mitigate all of the above-mentioned challenges. However, the intermittent nature of RE resources (such as solar, wind, hydro, geothermal and marine) leads to a challenge of high uncertainty output power. Hence, power demand cannot be reliably met, due to daily or seasonal weather changes. Therefore, a stand-alone RE system should comprise of an energy storage system (ESS), to store surplus energy for later use when the power demand is more than the generated output power. Additionally, a grid-interactive RE system should also comprise of the ESS, due to the variable tariff rates imposed by utility companies around the globe. The aim is to store excess energy during low-priced off-peak periods, for later use during high-priced peak periods. Hence, minimal electricity bill may be achieved by the consumers. The utility grid operator may also reap a benefit of a reduced blackout probability, especially during peak demanding periods. Among various RE technologies, hydrokinetic is a promising RE solution to be exploited in areas with flowing water resources, such as rivers, tidal current or artificial water channels. It is easily predictable and has proved to generate electricity at flowing water speeds, ranging from 0.5 m/s and above. It has proved to generate electricity markedly better and affordable than solar and wind energy systems. Furthermore, it has proved to operate cost-effectively, if it comprises of a pumped-hydro storage (PHS) system instead of a battery-based storage system. Rural consumers, such a farms, industries and mines situated in close proximity to flowing water resources, may make use of a grid-connected micro-hydrokinetic-pumped-hydro-storage (MHK-PHS) system to reduce electricity bills and sell the excess energy to the grid. However, a grid-connected MHK-PHS system requires a complex optimal energy management system, instead of expecting a consumer to respond to a change in real-time electricity price. The system should allow for optimal energy storage and sales, while ensuring that the consumer load demand is met at all times, by considering variable time-of-use (TOU) tariffs and load demand uncertainties that might take place in real-time context. This work deals with optimal energy management of a grid-connected MHK-PHS system, under different demand seasons for different load demand sectors, through the consideration of variable TOU tariffs. The aim is to minimize the customer electricity bills if the proposed system is approved to be non-interactive or interactive with the utility grid. Additionally, the alternative aim is to maximize the energy sales into the grid, if the system is grid-interactive. The results have proved that the developed optimization-based model is capable of minimizing the grid-cost, particularly during expensive peak-periods. Furthermore, the energy sales revenue has been maximized during peak-periods. Sundays have proved to lead to the largest amount of grid-power storage into the storage system, as compared to other days of the week. The industrial load profile led to the low net income, since most energy sales take place during the evening peak hours, instead of morning peak hours. However, if the load demand uncertainty constraint is considered, the above-mentioned open-loop optimization-based model has been unable to optimize the power flow. This led to the unmet load demand difficulty, as well as the excessive supply of power. Hence, an additional control model has been developed to assist the open-loop optimization-based model, to handle the load demand uncertainty disturbance in real-time context. The control model proved to mitigate the issue of both unmet load demand and excessive supply of power through the application of a rule-based algorithm. Additionally, a higher energy savings was achieved through the successful reduction of the excessively supplied power.